#1002 – Presidential Cycles and USD Exchange Rate<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> <\/strong>Monthly Source paper:<\/strong><\/p>\n\n\n\n Della Corte, Pasquale and Fu, Hsuan: Presidential Cycles and Exchange Rates<\/strong> #1003 – Same-Weekday Momentum<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> <\/strong>Daily Source paper:<\/strong><\/p>\n\n\n\n Da, Zhi and Zhang, Xiao, Same-Weekday Momentum<\/strong> #1004 – Using Internal Bar Strength for Trading Country ETFs<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> <\/strong>Intraday Source paper:<\/strong><\/p>\n\n\n\n Aditya Pandey, Kunal Joshi: Using Internal Bar Strength as a Key Indicator for Trading Country ETFs<\/strong> #1005 – Overnight Post-Earnings Announcement Drift<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> <\/strong>Intraday Source paper:<\/strong><\/p>\n\n\n\n Chan, Kam Fong and Marsh, Terry: Overnight Post-Earnings Announcement Drift and SEC Form 8-K Disclosures<\/strong> #1006 – Options Portfolio Selection with Position Limits<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> <\/strong>Monthly Source paper:<\/strong><\/p>\n\n\n\n Guasoni, Paolo and Guasoni, Paolo and Mayerhofer, Eberhard and Zhao, Mingchuan: Options Portfolio Selection with Position Limits<\/strong> #1007 – Intraday Residual Reversal in the U.S. Stock Market<\/a><\/strong><\/p>\n\n\n\n Period of rebalancing:<\/strong> <\/strong>Intraday Source paper:<\/strong><\/p>\n\n\n\n Brogaard, Jonathan and Han, Jaehee and Kim, Hanjun: Intraday Residual Reversal in the U.S. Stock Market<\/strong> #628 – Social Media Sentiment Factor<\/a><\/strong><\/p>\n\n\n\n Kang, Namho and Lou, Xiaoxia and Ozik, Gideon and Sadka, Ronnie and Shen, Siyi: Innocuous Noise? Social Media and Asset Prices<\/strong> #225 – Improved Merger Arbitrage<\/a><\/strong><\/p>\n\n\n\n Halskov, Kristoffer: Improving Merger Arbitrage Returns with Machine Learning<\/strong> #528 – Equity Factors and Corporate Bonds<\/a><\/strong> Zhai, Chongshuo: The study of Momentum Effect between China and US<\/strong> #906 – Using ChatGPT to Forecast Stock Price Movements<\/a><\/strong> Tan, Lin and Wu, Huihang and Zhang, Xiaoyan: Large Language Models and Return Prediction in China<\/strong> #406 – Cash-Based Operating Pro\ufb01tability<\/a><\/strong><\/p>\n\n\n\n Vyas, Aidan, Owner’s Earnings: Cash-based Operating Profits, and Capital Expenditures in the Cross Section of Stock Returns<\/strong> Corporate Bond Factors: Replication Failures and a New Framework<\/strong><\/a><\/p>\n\n\n\n The replication crisis in social sciences (and, of course, finance) is an often covered topic (see also our articles How do Investment Strategies Perform After Publication<\/a> and In-Sample vs. Out-of-Sample Analysis of Trading Strategies<\/a>). In vs. out-of-sample tests are usually performed on equity factors as data are available. However, the Copenhagen Business Schools, in close cooperation with AQR Capital Management, went in a different direction and built a database of realistic corporate bond data and took a closer look at the precision of corporate bonds forecasting methodologies. We applaud them for that, as working with the corporate bond data is challenging, and their work sheds a little light on this important part of the financial markets.<\/p>\n\n\n\n 800 Years on the Financial Markets<\/strong><\/a><\/p>\n\n\n\n Have we mentioned, that we love<\/a> history<\/a>? Probably<\/a> more<\/a> than<\/a> just<\/a> once<\/a>. What we like on the academic studies which use longterm data is that they offer a bird-like view on the financial markets. The daily noise and ebbs and flows retreat into the background and macroeconomic and geopolitical trends emerge. This top-down analysis helps to design the asset allocation or shape the overall structure of the portfolio of systematic trading strategies that may then trade on the higher frequency. Bryan Taylor\u2019s paper offers a treasure of tables and charts depicting over 800 years of history of returns of global stocks, bonds and bills.<\/p>\n\n\n\n What\u2019s the Size of the Risk Premia (from the Analysts\u2019 Perspective)<\/strong><\/a><\/p>\n\n\n\n The topic of today\u2019s short blog post concerns a subject that\u2019s connected to everybody participating in financial markets worldwide: different subjective return expectations. It is reasonable to have some expected returns you can count on if you are putting your money at risk. But how do they differ between different market professionals? And are return expectations influenced by recessions? We will look closely at financial analysts and their views on risk premia. The main point from the authors of the analyzed paper stresses the idea that analysts are counter-cyclical.<\/p>\n\n\n\n 994 – Trended Momentum<\/a><\/strong> Six new strategies have been added. Five new related research papers have been included into existing strategy reviews and three short free blog posts<\/strong><\/a> have been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect<\/strong><\/a> in the previous two weeks.<\/p>","protected":false},"author":25721,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-34205","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/34205","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/25721"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=34205"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/34205\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=34205"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=34205"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=34205"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Markets traded:<\/strong><\/strong> currencies
Instruments used for trading:<\/strong> <\/strong>CFDs, ETFs, forwards, futures
Complexity:<\/strong> <\/strong>Simple strategy
Backtest period:<\/strong> 1983-2024
Indicative performance:<\/strong> 5.43%
Estimated volatility:<\/strong> –<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4769498<\/a>
Abstract:
This paper shows that US presidential cycles can predict dollar-based exchange rate returns. Armed with more than 40 years of data and a large cross-section of currency pairs, we document an average US dollar appreciation during Democratic presidential terms and an average US dollar depreciation during Republican presidential mandates. The difference in these average exchange rate returns is larger than 5% per annum and is primarily linked to trade tariffs. In contrast, we find no relationship with cross-country interest rate differentials, inflation differentials, and pre-existing economic conditions. We relate these findings to trade policy within a model of exchange rate determination with constrained financiers.<\/p>\n\n\n\n
Markets traded:<\/strong><\/strong> equities
Instruments used for trading:<\/strong> <\/strong>stocks
Complexity:<\/strong> <\/strong>Complex strategy
Backtest period:<\/strong> 1963-2021
Indicative performance:<\/strong> 27.57%
Estimated volatility:<\/strong> 17.13%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4806275<\/a>
Abstract:
A disproportionately large fraction (70%) of stock momentum reflects return continuation on the same weekday (e.g., Mondays to Mondays), or the same-weekday momentum. Even accounting for partial reversals in other weekdays, the same-weekday momentum still contributes to a significant fraction (20% to 60%) of the momentum effect. This pattern is robust to different size filters, weighing schemes, time periods, and sample cuts. The same-weekday momentum is hard to square with traditional momentum theories based on investor mis-reaction. Instead, we provide direct and novel evidence that links it to within-week seasonality and persistence in institutional trading. Overall, our findings highlight institutional trading as an important driver of the stock momentum.<\/p>\n\n\n\n
Markets traded:<\/strong><\/strong> equities
Instruments used for trading:<\/strong> <\/strong>ETFs
Complexity:<\/strong> <\/strong>Complex strategy
Backtest period:<\/strong> 1963-2021
Indicative performance:<\/strong> 39.04%
Estimated volatility:<\/strong> 10%<\/p>\n\n\n\n
https:\/\/arxiv.org\/abs\/2306.12434<\/a>
Abstract:
This report aims to investigate the effectiveness of using internal bar strength (IBS) as a key indicator for trading country exchange-traded funds (ETFs). The study uses a quantitative approach to analyze historical price data for a bucket of country ETFs over a period of 10 years and uses the idea of Mean Reversion to create a profitable trading strategy. Our findings suggest that IBS can be a useful technical indicator for predicting short-term price movements in this basket of ETFs.<\/p>\n\n\n\n
Markets traded:<\/strong><\/strong> equities
Instruments used for trading:<\/strong> <\/strong>stocks
Complexity:<\/strong> <\/strong>Very complex strategy
Backtest period:<\/strong> 2004-2020
Indicative performance:<\/strong> 21.45%
Estimated volatility:<\/strong> 10%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4765828<\/a>
Abstract:
Companies reporting extreme quarterly earnings misses exhibit pronounced significant overnight drift post-announcement. We hypothesize that extreme earnings misses stimulate more intensive information acquisition throughout the reporting quarter, as those earnings announcements resolve a smaller fraction of information uncertainty. Using post-announcement SEC Form 8-K disclosures to proxy for additional material information, we show that information acquisition activities become more prevalent and substantial following extreme earnings misses. Concomitantly, the implied volatilities of these stocks remain elevated for much of the earnings quarter. Moreover, the incremental post-announcement Form 8-K information is unscheduled, mostly arriving overnight when market liquidity is low. These factors contribute to a higher overnight risk premium, and thus a pronounced average overnight drift post-earnings announcement.<\/p>\n\n\n\n
Markets traded:<\/strong><\/strong> equities
Instruments used for trading:<\/strong> <\/strong>options
Complexity:<\/strong> <\/strong>Very complex strategy
Backtest period:<\/strong> 1996-2020
Indicative performance:<\/strong> 21.05%
Estimated volatility:<\/strong> 14.52%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4287555<\/a>
Abstract:
This paper examines the performance from 1996 to 2020 of mean-variance efficient portfolios of monthly options with all available strikes on each of the S&P 500, Nasdaq 100, and Dow Jones indexes, using a constrained optimization approach that incorporates position limits, transaction costs, and volatility persistence. The Sharpe ratios of index-neutral strategies is between one and two for the S&P 500 and Nasdaq 100, but less than half for the Dow Jones. Constraining portfolios to be solvent on all past index\u2019 returns reduces Sharpe ratios by a third in the S&P 500 and Nasdaq 100 and by two thirds in the Dow Jones. All strategies suffer significant losses from the coronavirus shock of 2020, underscoring their vulnerability to rare events.<\/p>\n\n\n\n
Markets traded:<\/strong><\/strong> equities
Instruments used for trading:<\/strong><\/strong> stocks
Complexity:<\/strong> <\/strong>Very complex strategy
Backtest period:<\/strong> 1996-2022
Indicative performance:<\/strong> 88.82%
Estimated volatility:<\/strong> 45.71%<\/p>\n\n\n\n
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4731947<\/a>
Abstract:
Li et al. (2023) show that intraday risk factor exposure leads to predictable returns. In this paper, we focus on the unexplained price movements from the factor-based intraday model. We document an economically large and statistically significant return reversal based on the previous period\u2019s residual return. This residual reversal strategy, which buys stocks with negative residuals and sells stocks with positive residuals, earns an annualized return of 162.3%. The strategy captures the returns to liquidity provision to the transitory component of stock returns.<\/p>\n\n\n\nNew research papers related to existing strategies:<\/h5>\n\n\n\n
https:\/\/ssrn.com\/abstract=4747878<\/a>
Abstract:
This paper demonstrates that intense discussion on the Reddit social media platform reduces stock price informativeness. Increased social discussion reduces pre-earnings abnormal turnover and pre-earnings return drift and higher earnings response coefficients, indicating reduced price informativeness prior to announcements. Social discussion results in a delayed price correction of up to two months of well-documented anomalies; a corresponding trading strategy earns about 1% monthly. Traditional media do not generate similar effects, while the use of emojis intensifies it. Furthermore, firm managers rely less on stock prices in making firm real decisions when the stock is heavily discussed on social media. The findings suggest reduced production of value-relevant information in the presence of intense social discussion, highlighting the importance of understanding the emergence of such discussion.<\/p>\n\n\n\n
https:\/\/ssrn.com\/abstract=4765067<\/a>
Abstract:
A simple decomposition of the expected returns of merger arbitrage trades, whose individual parts are modelled by modern machine learning techniques, lead to better proxies for expected returns than realized merger arbitrage returns. These decomposed expected return estimates yield large economic gains for merger arbitrage investors in terms of both absolute and risk-adjusted returns. Furthermore, the decomposed expected return estimates grant better financial insight into the evolution of the merger arbitrage market, as well as the risk premium associated with each M&A deal.<\/p>\n\n\n\n
#568 – Momentum effect in Chinese B-shares<\/a><\/strong>
#938 – Daily Momentum in Chinese Equities<\/a><\/strong><\/p>\n\n\n\n
https:\/\/ssrn.com\/abstract=4673470<\/a>
Abstract:
I empirically replicate the momentum effect from 2000 to 2022, finding that the midterm momentum effect exists in U.S market but not in A-shares market. Turnover ratio is used to measure investor heterogeneity and explain the difference between China and U.S by doubling sorting, however, the result is not promising. Next, I document strong overnight and intraday return continuation along with an offsetting cross-period reversal effect, i.e. \u2018tug of war\u2019 effect. After linking investor heterogeneity to the persistence of the overnight and intraday components, I decompose the momentum return into these two parts, finding profits of momentum strategy in U.S market are earned entirely overnight, while profits of momentum strategy in A-shares market earned from both overnight and intraday. Finally, the difference of momentum effect between A-shares and U.S market is explained from the perspective of investor heterogeneity.<\/p>\n\n\n\n
#1000 – Sentiment trading with large language models<\/a><\/strong><\/p>\n\n\n\n
https:\/\/ssrn.com\/abstract=4712248<\/a>
Abstract:
We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that tones and return forecasts extracted by LLMs from news significantly predict future returns. The equal- and value-weighted long minus short portfolios yield annualized returns of 90% and 69% on average for the ensemble model. Given that these news articles are public information, the predictive power lasts about two days. More interestingly, the signals extracted by LLMs contain information about firm fundamentals, and can predict the aggressiveness of future trades. The predictive power is noticeably stronger for firms with less efficient information environment, such as firms with lower market cap, shorting volume, institutional and state ownership. These results suggest that LLMs are helpful in capturing under-processed information in public news, for firms with less efficient information environment, and thus contribute to overall market efficiency.<\/p>\n\n\n\n
https:\/\/ssrn.com\/abstract=4794183<\/a>
Abstract:
Owner\u2019s earnings, when defined as cash-based operating profits less capital expenditures, predicts the cross section of average stock returns and subsumes a variety of popular factors. This fact remains true regardless of the deflator employed. A strategy that purchases securities with high owner\u2019s earnings relative to a composite of given deflators and shorts their counterparts delivers significant alpha over the Fama-French 6-factor and q5 factor models (t-statistics of 6.57 and 2.51, respectively) and yields a Sharpe ratio of 1.19.<\/p>\n\n\n\nAnd several interesting free blog posts that have been published during the last 2 weeks:<\/h5>\n\n\n\n
Plus, the following trading strategies have been backtested in QuantConnect<\/a> in the previous two weeks:<\/h5>\n\n\n\n
995 – Pre-Refunding Announcement Gains in U.S. Treasuries<\/a><\/strong>
998 – Google Trends Sentiment as a Predictor for Cryptocurrency Returns<\/a><\/strong>
1002 – Presidential Cycles and USD Exchange Rate<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"